C-Link: A Hierarchical Clustering Approach to Large-scale Near-optimal Coalition Formation
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 106-111.
Abstract
Coalition formation is a fundamental approach to multi-agent coordination. In this paper we address the specific problem of coalition structure generation, and focus on providing good-enough solutions using a novel heuristic approach that is based on data clustering methods. In particular, we propose a hierarchical agglomerative clustering approach (C-Link), which uses a similarity criterion between coalitions based on the gain that the system achieves if two coalitions merge. We empirically evaluate C-Link on a synthetic benchmark data-set as well as in collective energy purchasing settings. Our results show that the C-link approach performs very well against an optimal benchmark based on Mixed-Integer Programming, achieving solutions which are in the worst case about 80% of the optimal (in the synthetic data-set), and 98% of the optimal (in the energy data-set). Thus we show that C-Link can return solutions for problems involving thousands of agents within minutes.
@inproceedings{ijcai2013p27,
title = {C-Link: A Hierarchical Clustering Approach to Large-scale Near-optimal Coalition Formation},
author = {Farinelli, Alessandro and Bicego, Manuele and Ramchurn, Sarvapali D. and Zucchelli, Mauro},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {106--111},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/27},
}
TY - CPAPER TI - C-Link: A Hierarchical Clustering Approach to Large-scale Near-optimal Coalition Formation AU - Farinelli, Alessandro AU - Bicego, Manuele AU - Ramchurn, Sarvapali D. AU - Zucchelli, Mauro T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 106 EP - 111 UR - https://ijcai.org/proceedings/2013/27 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: